In a world where connectivity shapes opportunity, owning a mobile phone is no longer a luxury-it is a lifeline. Yet, millions remain disconnected, locked out of essential services, education, healthcare, and economic possibilities. This report explores the global landscape of mobile telephone ownership, revealing striking inequalities across countries, genders, and generations. Through the lens of data, we witness not just numbers, but the quiet stories of progress and the urgent gaps that demand action. Because in today’s world, to be unreachable is to be left behind.
Numbers alone rarely move us, but when translated into visuals, they become powerful narratives of connection, inequality, and progress. In this section, we delve deeper into the patterns and stories hidden within the data. A world map will reveal the stark contrasts between countries, highlighting where mobile ownership thrives and where it lags behind. A timeline will trace humanity’s growing bond with technology, showing both rapid advancements and areas of stagnation. We will also explore gender disparities, where access is not just about geography but also about deeply rooted societal divides. Finally, we will follow the trends across nations, observing how some are sprinting toward full connectivity while others are left behind. Through these visualizations, we witness more than statistics — we see lives, opportunities, and the profound impact that a simple device can have in bridging the world together.
# Create world map
fig = px.choropleth(
country_totals,
locations="alpha_3_code",
color="obs_value",
hover_name="country",
color_continuous_scale=custom_colors,
title="Proportion of Individuals Who Own a Mobile Telephone",
labels={'obs_value': 'Ownership (%)'},
projection="natural earth"
)
fig.update_layout(margin={"r":0,"t":40,"l":0,"b":0})
fig.show()Global distribution of mobile telephone ownership (latest available data)
Mobile phone ownership across the world shows stark contrasts. Southeast Asia lags behind in ownership, with many countries struggling due to infrastructure and affordability issues. Europe, on the other hand, is strong, with near-total ownership in most nations. The Middle East presents a mixed picture—while some countries, like the UAE and Qatar, have high mobile ownership, others remain significantly behind, reflecting a gap in economic development. In the Americas, countries like the U.S. and Canada are on the positive side, with high ownership rates, while parts of Latin America show slower progress. This global map highlights the uneven spread of mobile ownership, revealing both the potential for growth and the challenges that remain.
# Prepare data for scatter plot
scatter_df = df[df['sex'] == 'Total'].dropna(subset=['time_period', 'obs_value'])
fig = px.scatter(scatter_df,
x="time_period",
y="obs_value",
trendline="lowess",
color_discrete_sequence=custom_colors ,
title="Mobile Telephone Ownership Over Time with Trend Line",
labels={'time_period': 'Year', 'obs_value': 'Ownership (%)'})
fig.update_traces(marker=dict(size=5, opacity=0.5))
fig.update_layout(xaxis_range=[2010,2025])
fig.show()Mobile ownership by year with regression line showing global trend
The scatter plot showing mobile telephone ownership over time illustrates the rapid rise of mobile technology globally. Initially slow, the growth curve steepens as mobile phones became more affordable and essential, particularly from the 2000s onward. The trend line highlights a sharp increase in ownership, especially in emerging economies where mobile phones became crucial for education and economic opportunities. While regions like North America and Europe show near-total ownership, other areas still lag, reflecting regional disparities. The plot captures the ongoing global shift towards universal mobile connectivity and the need to bridge the digital divide.
# Get countries with both male and female data
gender_df = latest_data[latest_data['sex'].isin(['Male', 'Female'])]
top_countries = gender_df['country'].value_counts().head(20).index
gender_comparison = gender_df[gender_df['country'].isin(top_countries)]
fig = px.bar(gender_comparison,
x="country",
y="obs_value",
color="sex",
barmode="group",
title="Mobile Ownership by Gender in Selected Countries",
labels={'obs_value': 'Ownership (%)', 'country': ''},
color_discrete_sequence=custom_colors )
fig.update_layout(xaxis={'categoryorder':'total descending'})
fig.show()Comparison of mobile ownership between genders for selected countries
The map and bar chart showing mobile ownership by gender in selected countries highlight global disparities in mobile phone access. The map visualizes regional variations, with certain countries showing nearly equal ownership between genders, while others display significant gender gaps. The bar chart further breaks this down, illustrating how women in some regions face lower mobile ownership compared to men, often due to economic, cultural, or infrastructure challenges. This data emphasizes the need for targeted policies to ensure equal access to mobile technology for all genders.
# Select countries with most data points
countries_with_most_data = df['country'].value_counts().head(10).index
trend_df = df[df['country'].isin(countries_with_most_data) & (df['sex'] == 'Total')]
fig = px.line(trend_df,
x="time_period",
y="obs_value",
color="country",
color_discrete_sequence=custom_colors,
title="Mobile Ownership Trends Over Time",
labels={'time_period': 'Year', 'obs_value': 'Ownership (%)'})
fig.update_layout(legend_title_text='Country')
fig.show()Trend of mobile ownership over time for selected countries
The “Mobile Ownership Trends Over Time” chart tracks the steady global rise in mobile phone ownership. Initially slow, the growth accelerates as technology becomes more affordable and accessible. The chart shows significant increases in developing regions, where mobile phones have become essential for communication, education, and economic growth. While developed countries reached high ownership levels early, emerging economies are catching up, though disparities remain, highlighting the ongoing global push for universal mobile access.
df['ownership_bin'] = pd.cut(df['obs_value'],
bins=[0, 20, 40, 60, 80, 100],
labels=['0-20%', '20-40%', '40-60%', '60-80%', '80-100%'])
area_df = df.groupby(['time_period', 'ownership_bin']).size().reset_index(name='count')
fig = px.area(area_df,
x="time_period",
y="count",
color="ownership_bin",
title="Distribution of Mobile Ownership Percentages Over Time",
labels={'time_period': 'Year', 'count': 'Number of Countries'},
color_discrete_sequence=custom_colors)
fig.update_layout(legend_title_text='Ownership Range')
fig.show()The “Distribution of Mobile Ownership Percentages Over Time” chart reveals how mobile ownership has spread globally. Over the years, the chart shows a gradual shift from low ownership in earlier years to widespread adoption, particularly in the last decade. The distribution highlights increasing equality in mobile access, though disparities still exist between developed and developing regions, with some areas showing slower progress. This shift underscores the growing importance of mobile phones in daily life and the ongoing efforts to ensure global connectivity.
In conclusion, mobile ownership trends reveal a transformative shift in global connectivity, with rapid growth in adoption across both developed and developing regions. While significant progress has been made, particularly in mobile-driven economies, disparities persist, especially in rural and low-income areas. The ongoing expansion of mobile access is crucial for bridging the digital divide, unlocking economic opportunities, and fostering global communication. As mobile technology continues to evolve, ensuring equal access for all will be key to shaping a more connected and inclusive future.